Contact distribution function based clustering technique with self-organizing maps
Автор: G. Chamundeswari, G. P. S. Varma, Ch. Satyanarayana
Журнал: International Journal of Image, Graphics and Signal Processing @ijigsp
Статья в выпуске: 3 vol.10, 2018 года.
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Currently clustering techniques play a vital role in object recognition process. The clustering techniques are found to be efficient with neural networks. So, the present paper proposed a novel method for clustering the input objects with Self-Organizing Map (SOM). The proposed method considers the input object as a random closed set. The random set can be efficiently described with various features viz., volume fractions, co-variance and contact distributions etc. In the proposed method, the input object is described efficiently with spherical contact distribution. The proposed method is experimented with the leaf data set with 795 images. The performance of the proposed method is evaluated with various topologies of SOM and is measured with four measures viz., FNR, FPR, TPR and TNR. The results indicate the efficiency of the proposed method.
Spherical contact distribution, Linear contact distribution, Feature vector, Neighborhood and Topology
Короткий адрес: https://sciup.org/15015944
IDR: 15015944 | DOI: 10.5815/ijigsp.2018.03.02
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